Evaluating and Explaining Climate Science

Archive for the ‘Impacts’ Category

In recent articles we have looked at rainfall and there is still more to discuss. This article changes tack to look at tropical cyclones, prompted by the recent US landfall of Harvey and Irma along with questions from readers about attribution and the future.

It might be surprising to find the following statement from leading climate scientists (Kevin Walsh and many co-authors in 2015):

At present, there is no climate theory that can predict the formation rate of tropical cyclones from the mean climate state.

The subject gets a little involved so let’s dig into a few papers. First from Gabriel Vecchi and some co-authors in 2008 in the journal Science. The paper is very brief and essentially raises one question – has the recent rise in total Atlantic cyclone intensity been a result of increases in absolute sea surface temperature (SST) or relative sea surface temperature:

From Vecchi et al 2008

Figure 1

The top graph (above) shows a correlation of 0.79 between SST and PDI (power dissipation index). The bottom graph shows a correlation of 0.79 between relative SST (local sea surface temperature minus the average tropical sea surface temperature) and PDI.

With more CO2 in the atmosphere from burning fossil fuels we expect a warmer SST in the tropical Atlantic in 2100 than today. But we don’t expect the tropical Atlantic to warm faster than the tropics in general.

If cyclone intensity is dependent on local SST we expect more cyclones, or more powerful cyclones. If cyclone intensity is dependent on relative SST we expect no increase in cyclones. This is because climate models predict warmer SSTs in the future but not warmer Atlantic SSTs than the tropics. The paper also shows a few high resolution models – green symbols – sitting close to the zero change line.

Now predicting tropical cyclones with GCMs has a fundamental issue – the scale of a modern high resolution GCM is around 100km. But cyclone prediction requires a higher resolution due to their relatively small size.

Thomas Knutson and co-authors (including the great Isaac Held) produced a 2007 paper with an interesting method (of course, the idea is not at all new). They input actual meteorological data (i.e. real history from NCEP reanalysis) into a high resolution model which covered just the Atlantic region. Their aim was to see how well this model could reproduce tropical storms. There are some technicalities to the model – the output is constantly “nudged” back towards the actual climatology and out at the boundaries of the model we can’t expect good simulation results. The model resolution is 18km.

The main question addressed here is the following: Assuming one has essentially perfect knowledge of large-scale atmospheric conditions in the Atlantic over time, how well can one then simulate past variations in Atlantic hurricane activity using a dynamical model?

They comment that the cause of the recent (at that time) upswing in hurricane activity “remains unresolved”. (Of course, fast forward to 2016, prior to the recent two large landfall hurricanes, and the overall activity is at a 1970 low. In early 2018, this may be revised again..).

Two interesting graphs emerge. First an excellent match between model and observations for overall frequency year on year:

From Knutson et al 2007

Figure 2

Second, an inability to predict the most intense hurricanes. The black dots are observations, the red dots are simulations from the model. The vertical axis, a little difficult to read, is SLP, or sea level pressure:

From Knutson et al 2007

Figure 3

These results are a common theme of many papers – inputting the historical climatological data into a model we can get some decent results on year to year variation in tropical cyclones. But models under-predict the most intense cyclones (hurricanes).

Here is Morris Bender and co-authors (including Thomas Knutson, Gabriel Vecchi – a frequent author or co-author in this genre, and of course Isaac Held) from 2010:

Some statistical analyses suggest a link between warmer Atlantic SSTs and increased hurricane activity, although other studies contend that the spatial structure of the SST change may be a more important control on tropical cyclone frequency and intensity. A few studies suggest that greenhouse warming has already produced a substantial rise in Atlantic tropical cyclone activity, but others question that conclusion.

This is a very typical introduction in papers on this topic. I note in passing this is a huge blow to the idea that climate scientists only ever introduce more certainty and alarm on the harm from future CO2 emissions. They don’t. However, it is also true that some climate scientists believe that recent events have been accentuated due to the last century of fossil fuel burning and these perspectives might be reported in the media. I try to ignore the media and that is my recommendation to readers on just about all subjects except essential ones like the weather and celebrity news.

This paper used a weather prediction model starting a few days before each storm to predict the outcome. If you understand the idea behind Knutson 2007 then this is just one step further – a few days prior to the emergence of an intense storm, input the actual climate data into a high resolution model and see how well the high res model predicts the observations. They also used projected future climates from CMIP3 models (note 1).

In the set of graphs below there are three points I want to highlight – and you probably need to click on the graph to enlarge it.

First, in graph B, “Zetac” is the model used by Knutson et al 2007, whereas GFDL is the weather prediction model getting better results in this paper – you can see that observations and the GFDL are pretty close in the maximum wind speed distribution. Second, the climate change predictions in E show that predictions of the future show an overall reduction in frequency of tropical storms, but an increase in the frequency of storms with the highest wind speeds – this is a common theme in papers from this genre. Third, in graph F, the results (from the weather prediction model) fed by different GCMs for future climate show quite different distributions. For example, the UKMO model produces a distribution of future wind speeds that is lower than current values.

From Bender et al 2010

Figure 4 – Click to enlarge

In this graph (S3 from the Supplementary data) we see graphs of the difference between future projected climatologies and current climatologies for three relevant parameters for each of the four different models shown in graph F in the figure above:

From Bender et al 2010

Figure 5 – Click to enlarge

This illustrates that different projected future climatologies, which all show increased SST in the Atlantic region, generate quite different hurricane intensities. The paper suggests that the reduction in wind shear in the UKMO model produces a lower frequency of higher intensity hurricanes.

Conclusion

This article illustrates that feeding higher resolution models with current data can generate realistic cyclone data in some aspects, but less so in other aspects. As we increase the model resolution we can get even better results – but this is dependent on inputting the correct climate data. As we look towards 2100 the questions are – How realistic is the future climate data? How does that affect projections of hurricane frequencies and intensities?

In XII – Rainfall 2 we saw the results of many models on rainfall as GHGs increase. They project wetter tropics, drier subtropics and wetter higher latitude regions. We also saw an expectation that rainfall will increase globally, with something like 2-3% per ºC of warming.

Here is a (too small) graph from Allen & Ingram (2002) showing the model response of rainfall under temperature changes from GHG increases. The dashed line marked “C-C” is the famous (in climate physics) Clausius–Clapeyron relation which, at current temperatures, shows a 7% change in water vapor per ºC of warming. The red triangles are the precipitation changes from model simulations showing about half of that.

From Allen & Ingram (2002)

Figure 1

Here is another graph from the same paper showing global mean temperature change (top) and rainfall over land (bottom):

From Allen & Ingram (2002)

Figure 2

The temperature has increased over the last 50 years, and models and observations show that the precipitation has.. oh, it’s not changed. What is going on?

First, the authors explain some important background:

The distribution of moisture in the troposphere (the part of the atmosphere that is strongly coupled to the surface) is complex, but there is one clear and strong control: moisture condenses out of supersaturated air.

This constraint broadly accounts for the humidity of tropospheric air parcels above the boundary layer, because almost all such parcels will have reached saturation at some point in their recent history. Physically, therefore, it has long seemed plausible that the distribution of relative humidity would remain roughly constant under climate change, in which case the Clausius-Clapeyron relation implies that specific humidity would increase roughly exponentially with temperature.

This reasoning is strongest at higher latitudes where air is usually closer to saturation, and where relative humidity is indeed roughly constant through the substantial temperature changes of the seasonal cycle. For lower latitudes it has been argued that the real-world response might be different. But relative humidity seems to change little at low latitudes under a global warming scenario, even in models of very high vertical resolution, suggesting this may be a robust ’emergent constraint’ on which models have already converged.

They continue:

If tropospheric moisture loading is controlled by the constraints of (approximately) unchanged relative humidity and the Clausius-Clapeyron relation, should we expect a corresponding exponential increase in global precipitation and the overall intensity of the hydrological cycle as global temperatures rise?

This is certainly not what is observed in models.

To clarify, the point in the last sentence is that models do show an increase in precipitation, but not at the same rate as the expected increase in specific humidity (see note 1 for new readers).

They describe their figure 2 (our figure 1 above) and explain:

The explanation for these model results is that changes in the overall intensity of the hydrological cycle are controlled not by the availability of moisture, but by the availability of energy: specifically, the ability of the troposphere to radiate away latent heat released by precipitation.

At the simplest level, the energy budgets of the surface and troposphere can be summed up as a net radiative heating of the surface (from solar radiation, partly offset by radiative cooling) and a net radiative cooling of the troposphere to the surface and to space (R) being balanced by an upward latent heat flux (LP, where L is the latent heat of evaporation and P is global-mean precipitation): evaporation cools the surface and precipitation heats the troposphere.

[Emphasis added].

Basics Digression

Picture the atmosphere over a long period of time (like a decade), and for the whole globe. If it hasn’t heated up or cooled down we know that the energy in must equal energy out (or if it has only done so only marginally then energy in is almost equal to energy out). This is the first law of thermodynamics – energy is conserved.

What energy comes into the atmosphere?

Solar radiation is partly absorbed by the atmosphere (most is transmitted through and heats the surface of the earth)

Radiation emitted from the earth’s surface (we’ll call this terrestrial radiation) is mostly absorbed by the atmosphere (some is transmitted straight through to space)

Warm air is convected up from the surface

Heat stored in evaporated water vapor (latent heat) is convected up from the surface and the water vapor condenses out, releasing heat into the atmosphere when this happens

How does the atmosphere lose energy?

It radiates downwards to the surface

It radiates out to space

..end of digression

Changing Energy Budget

In a warmer world, if we have more evaporation we have more latent heat transfer from the surface into the troposphere. But the atmosphere has to be able to radiate this heat away. If it can’t, then the atmosphere becomes warmer, and this reduces convection. So with a warmer surface we may have a plentiful potential supply of latent heat (via water vapor) but the atmosphere needs a mechanism to radiate away this heat.

Allen & Ingram put forward a simple conceptual equation:

ΔRc + ΔRT = LΔP

where the change in radiative cooling ΔR, is split into two components: ΔRc that is independent of the change in atmospheric temperature; and ΔRT that depends only on the temperature

Now, if we double CO2, then before any temperature changes we decrease the outgoing longwave radiation through the tropopause (the top of the troposphere) by about 3-4W/m² and we increase atmospheric radiation to the surface by about 1W/m².

So doubling CO2, ΔRc = -2 to -3W/m²; prior to a temperature change ΔRT = 0; and so ΔP reduces.

The authors comment that increasing CO2 before any temperature change takes place reduces the intensity of the hydrological cycle and this effect was seen in early modeling experiments using prescribed sea surface temperatures.

Now, of course, the idea of doubling CO2 without any temperature change is just a thought experiment. But it’s an important thought experiment because it lets us isolate different factors.

The authors then consider their factor ΔRT:

The enhanced radiative cooling due to tropospheric warming, ΔRT, is approximately proportional to ΔT: tropospheric temperatures scale with the surface temperature change and warmer air radiates more energy, so ΔRT = kΔT, with k=3W/(m²K)..

All this is saying is that as the surface warms, the atmosphere warms at about the same rate, and the atmosphere then emits more radiation. This is why the model results of rainfall in our figure 2 above show no trend in rainfall over 50 years, and also match the observations – the constraint on rainfall is the changing radiative balance in the troposphere.

And so they point out:

Thus, although there is clearly usable information in fig. 3 [our figure 2], it would be physically unjustified to estimate ΔP/ΔT directly from 20th century observations and assume that the same quantity will apply in the future, when the balance between climate drivers will be very different.

There is a lot of other interesting commentary in their paper, although the paper itself is now quite dated (and unfortunately behind a paywall). In essence they discuss the difficulties of modeling precipitation changes, especially for a given region, and are looking for “emergent constraints” from more fundamental physics that might help constrain forecasts.

A forecasting system that rules out some currently conceivable futures as unlikely could be far more useful for long-range planning than a small number of ultra-high-resolution forecasts that simply rule in some (very detailed futures as possibilities).

This is a very important point when considering impacts.

Conclusion

Increasing the surface temperature by 1ºC is expected to increase the humidity over the ocean by about 7%. This is simply the basic physics of saturation. However, climate models predict an increase in mean rainfall of maybe 2-3% per ºC. The fundamental reason is that the movement of latent heat from the surface to the atmosphere has to be radiated away by the atmosphere, and so the constraint is the ability of the atmosphere to do this. And so the limiting factor in increasing rainfall is not the humidity increase, it is the radiative cooling of the atmosphere.

We also see that despite 50 years of warming, mean rainfall hasn’t changed. Models also predict this. This is believed to be a transient state, for reasons explained in the article.

References

Notes

1 Relative humidity is measured as a percentage. If the relative humidity = 100% it means the air is saturated with water vapor – it can’t hold any more water vapor. If the relative humidity = 0% it means the air is completely dry. As temperature increases the ability of air to hold water vapor increases non-linearly.

For example, at 0ºC, 1kg of air can carry around 4g of water vapor, at 10ºC that has doubled to 8g, and at 20ºC it has doubled again to 15g (I’m using approximate values).

So now imagine saturated air over the ocean at 20ºC rising up and therefore cooling (it is cooler higher up in the atmosphere). By the time the air parcel has cooled down to 0ºC (this might be anything from 2km to 5km altitude) it is still saturated but is only carrying 4g of water vapor, having condensed out 11g into water droplets.

I probably should have started a separate series on rainfall and then woven the results back into the Impacts series. It might take a few articles working through the underlying physics and how models and observations of current and past climate compare before being able to consider impacts.

There are a number of different ways to look at rainfall models and reality:

As an introduction, the underlying physics perhaps provides some constraints. This is strongly believed in the modeling community. The constraint is a simple one – if we warm the ocean by 1K (= 1ºC) then the amount of water vapor above the ocean surface increases by about 7%. So we expect a warmer world to have more water vapor – at least in the boundary layer (typically 1km) and over the ocean. If we have more water vapor then we expect more rainfall. But GCMs and also simple models suggest a lower value, like 2-3% per K, not 7%/K. We will come back to why in another article.

It also seems from models that with global warming, rainfall increases more in regions and times of already high rainfall and reduces in regions and times of low rainfall – the “wet get wetter and the dry get drier”. (Also a marketing mantra that introducing a catchy slogan ensures better progress of an idea). So we also expect changes in the distribution of rainfall. One reason for this is a change in the tropical circulation. All to be covered later, so onto the paper..

We analyze the outputs of 14 CMIP5 models based on a 140 year experiment with a prescribed 1% per year increase in CO2 emission. This rate of CO2 increase is comparable to that prescribed for the RCP8.5, a relatively conservative business-as-usual scenario, except the latter includes also changes in other GHG and aerosols, besides CO2.

A 27-year period at the beginning of the integration is used as the control to compute rainfall and temperature statistics, and to compare with climatology (1979–2005) of rainfall data from the Global Precipitation Climatology Project (GPCP). Two similar 27-year periods in the experiment that correspond approximately to a doubling of CO2 emissions (DCO2) and a tripling of CO2 emissions (TCO2) compared to the control are chosen respectively to compute the same statistics..

Just a note that I disagree with the claim that RCP8.5 is a “relatively conservative business as usual scenario” (see Impacts – II – GHG Emissions Projections: SRES and RCP), but that’s just an opinion, as are all views about where the world will be in population, GDP and cumulative emissions 100-150 years from now. It doesn’t detract from the rainfall analysis in the paper.

For people wondering “what is CMIP5?” – this is the model inter-comparison project for the most recent IPCC report (AR5) where many models have to address the same questions so they can be compared.

Here we see (and along with other graphs you can click to enlarge) what the models show in temperature (top left), mean global rainfall (top right), zonal rainfall anomaly by latitude (bottom left) and the control vs the tripled CO2 comparison (bottom right). The many different colors in the first three graphs are each model, while the black line is the mean of the models (“ensemble mean”). The bottom right graph helps put the changes shown in the bottom left into a perspective – with the different between the red and the blue being the difference between tripling CO2 and today:

From Lau et al 2013

Figure 1 – Click to enlarge

In the figure above, the bottom left graph shows anomalies. We see one of the characteristics of models as a result of more GHGs – wetter tropics and drier sub-tropics, along with wetter conditions at higher latitudes.

From the supplementary material, below we see a better regional breakdown of fig 1d (bottom right in the figure above). I’ll highlight the bottom left graph (c) for the African region. Over the continent, the differences between present day and tripling CO2 seem minor as far as model predictions go for mean rainfall:

From Lau et al 2013

Figure 2 – Click to enlarge

The supplementary material also has a comparison between models and observations. The first graph below is what we are looking at (the second graph we will consider afterwards). TRMM (Tropical Rainfall Measuring Mission) is satellite data and GPCP one rainfall climatology that we met in the last article – so they are both observational datasets. We see that the models over-estimate tropic rainfall, especially south of the equator:

From Lau et al 2013

Figure 3 – Click to enlarge

Rainfall Distribution from Light through to Heavy Rain

Lau and his colleagues then look at rainfall distribution in terms of light rainfall through to heavier rainfall. So, take global rainfall and divide it into frequency of occurrence, with light rainfall to the left and heavy rainfall to the right. Take a look back at the bottom graph in the figure above (figure 3, their figure S1). Note that the horizontal axis is logarithmic, with a ratio of over 1000 from left to right.

It isn’t an immediately intuitive graph. Basically there are two sets of graphs. The left “cluster” is how often that rainfall amount occurred, and the black line is GPCP observations. The “right cluster” is how much rainfall fell (as a percentage of total rainfall) for that rainfall amount and again black is observations.

So lighter rainfall, like 1mm/day and below accounts for 50% of time, but being light rainfall accounts for less than 10% of total rainfall.

To facilitate discussion regarding rainfall characteristics in this work, we define, based on the ensemble model PDF, three major rain types: light rain (LR), moderate rain (MR), and heavy rain (HR) respectively as those with monthly mean rain rate below the 20th percentile (<0.3 mm/day), between response (TCO2 minus control, black) and the inter-model 1s the 40th–70th percentile (0.9–2.4mm/day), and above the 98.5% percentile (>9mm/day). An extremely heavy rain (EHR) type defined at the 99.9th percentile (>24 mm day1) will also be referred to, as appropriate.

Here is a geographical breakdown of the total and then the rainfall in these three categories, model mean on the left and observations on the right:

From Lau et al 2013

Figure 4 – Click to enlarge

We can see that the models tend to overestimate the heavy rain and underestimate the light rain. These graphics are excellent because they help us to see the geographical distribution.

Now in the graphs below we see at the top the changes in frequency of mean precipitation (60S-60N) as a function of rain rate; and at the bottom we see the % change in rainfall per K of temperature change, again as a function of rain rate. Note that the bottom graph also has a logarithmic scale for the % change, so as you move up each grid square the value is doubled.

The different models are also helpfully indicated so the spread can be seen:

From Lau et al 2013

Figure 5 – Click to enlarge

Notice that the models are all predicting quite a high % change in rainfall per K for the heaviest rain – something around 50%. In contrast the light rainfall is expected to be up a few % per K and the medium rainfall is expected to be down a few % per K.

Globally, rainfall increases by 4.5%, with a sensitivity (dP/P/dT) of 1.4% per K

Here is a table from their supplementary material with a zonal breakdown of changes in mean rainfall (so not divided into heavy, light etc). For the non-maths people the first row, dP/P is just the % change in precipitation (“d” in front of a variable means “change in that variable”), the second row is change in temperature and the third row is the % change in rainfall per K (or ºC) of warming from GHGs:

And as a result of these projections, the authors also show the number of dry months and the projected changes in number of dry months:

From Lau et al 2013

Figure 8 – Click to enlarge

The authors conclude:

The IPCC CMIP5 models project a robust, canonical global response of rainfall characteristics to CO2 warming, featuring an increase in heavy rain, a reduction in moderate rain, and an increase in light rain occurrence and amount globally.

For a scenario of 1% CO2 increase per year, the model ensemble mean projects at the time of approximately tripling of the CO2 emissions, the probability of occurring of extremely heavy rain (monthly mean >24mm/day) will increase globally by 100%–250%, moderate rain will decrease by 5%–10% and light rain will increase by 10%–15%.

The increase in heavy rain is most pronounced in the equatorial central Pacific and the Asian monsoon regions. Moderate rain is reduced over extensive oceanic regions in the subtropics and extratropics, but increased over the extratropical land regions of North America, and Eurasia, and extratropical Southern Oceans. Light rain is mostly found to be inversely related to moderate rain locally, and with heavy rain in the central Pacific.

The model ensemble also projects a significant global increase up to 16% more frequent in the occurrences of dry months (drought conditions), mostly over the subtropics as well as marginal convective zone in equatorial land regions, reflecting an expansion of the desert and arid zones..

..Hence, the canonical global rainfall response to CO2 warming captured in the CMIP5 model projection suggests a global scale readjustment involving changes in circulation and rainfall characteristics, including possible teleconnection of extremely heavy rain and droughts separated by far distances. This adjustment is strongly constrained geographically by climatological rainfall pattern, and most likely by the GHG warming induced sea surface temperature anomalies with unstable moister and warmer regions in the deep tropics getting more heavy rain, at the expense of nearby marginal convective zones in the tropics and stable dry zones in the subtropics.

Conclusion

This article has basically presented the results of one paper, which demonstrates consistency in model response of rainfall to doubling and tripling of CO2 in the atmosphere. In subsequent articles we will look at the underlying physics constraints, at time-series over recent decades and try to make some kind of assessment.

References

Further Reading

Here are a bunch of papers that I found useful for readers who want to dig into the subject. Most of them are available for free via Google Scholar, but one of the most helpful to me (first in the list) was Allen & Ingram 2002 and the only way I could access it was to pay $4 to rent it for a couple of days.

If we want to assess forecasts of floods, droughts and crop yields then we will need to know rainfall. We will also need to know temperature of course.

The forte of climate models is temperature. Rainfall is more problematic.

Before we get to model predictions about the future we need to review observations and the ability of models to reproduce them. Observations are also problematic – rainfall varies locally and over short durations. And historically we lacked effective observation systems in many locations and regions of the world, so data has to be pieced together and estimated from reanalysis.

Smith and his colleagues created a new rainfall dataset. Here is a comment from their 2012 paper:

Although many land regions have long precipitation records from gauges, there are spatial gaps in the sampling for undeveloped regions, areas with low populations, and over oceans. Since 1979 satellite data have been used to fill in those sampling gaps. Over longer periods gaps can only be filled using reconstructions or reanalyses..

Here are two views of the global precipitation data from a dataset which starts with the satellite era, that is, 1979 onwards – GPCP (Global Precipitation Climatology Project):

From Adler et al 2003

Figure 1

From Adler et al 2003

Figure 2

For historical data before satellites we only have rain gauge data. The GPCC dataset, explained in Becker et al 2013, shows the number of stations over time by region:

From Becker et al 2013

Figure 3- Click to expand

And the geographical distribution of rain gauge stations at different times:

From Becker et al 2013

Figure 4 – Click to expand

The IPCC compared the global trends over land from four different datasets over the last century and the last half-century:

From IPCC AR5 Ch. 2

Figure 5 – Click to expand

And the regional trends:

From IPCC AR5 Ch. 2

Figure 6 – Click to expand

The graphs for the annual change in rainfall, note the different scales for each region (as we would expect given the difference in average rainfall in different region):

From IPCC AR5 ch 2

Figure 7

We see that the decadal or half-decadal variation is much greater than any apparent long term trend. The trend data (as reviewed by the IPCC in figs 5 & 6) shows significant differences in the datasets but when we compare the time series it appears that the datasets match up better than indicated by the trend comparisons.

The data with the best historical coverage is 30ºN – 60ºN and the trend values for 1951-2000 (from different reconstructions) range from an annual increase of 1 to 1.5 mm/yr per decade (fig 6 / table 2.10 of IPCC report). This is against an absolute value of about 1000 mm/yr in this region (reading off the climatology in figure 2).

This is just me trying to put the trend data in perspective.

Models

Here is the IPCC AR5 chapter 9 on model comparisons to satellite-era rainfall observations. Top left is observations (basically the same dataset as figure 1 in this article over a slightly longer period with different colors) and bottom right is percentage error of model average with respect to observations:

From IPCC AR5 ch 9

Figure 8 – Click to expand

We can see that the average of all models has substantial errors on mean rainfall.

Sea-level rise (SLR) poses a particularly ominous threat because 10% of the world’s population (634 million people) lives in low-lying coastal regions within 10 m elevation of sea level (McGranahan et al. 2007). Much of this population resides in portions of 17 of the world’s 30 largest cities, including Mumbai, India; Shanghai, China; Jakarta, Indonesia; Bangkok, Thailand; London; and New York.

In the last article – Sinking Megacities – we saw that some of these cities are sinking due to ground water depletion. To those megacities, this is a much more serious threat than global sea level rise (probably why we see so many marches and protests about ground water depletion).

The paper continues:

..The potential loss of life in low-lying areas is even more graphically illustrated by the 1970 Bhola cyclone that traveled northward through the Bay of Bengal producing a 12-m-high wall of water that drowned a half million people in East Pakistan (now Bangladesh) (Garrison 2005).

In Bangladesh, storms and cyclones are much more of a threat than sea level rise. Here is Karim and Mimura (2008) listing the serious cyclones over the last 60 years:

From Karim and Mimura 2008

Figure 1 – Click to expand

There is an interesting World Bank Report from 2011. First on floods:

In an average year, nearly one quarter of Bangladesh is inundated, with more than three-fifths of land area at risk of floods of varying intensity (Ahmed and Mirza 2000). Every four or five years, a severe flood occurs during the monsoon season, submerging more than three-fifths of the land..

The most recent exceptional flood, which occurred in 2007, inundated 62,300 km² or 42 percent of total land area, causing 1,110 deaths and affecting 14 million people; 2.1 million ha of standing crop land were submerged, 85,000 houses completely destroyed, and 31,533 km of roads damaged. Estimated asset losses from this one event totaled US$1.1 billion (BWDB 2007).

Flooding in Bangladesh results from a complex set of factors, key among which are extremely low and flat topography, uncertain transboundary flow, heavy monsoon rainfall, and high vulnerability to tidal waves and congested drainage channels. Two-thirds of Bangladesh’s land area is less than 5 m above sea level. Each year, an average flow of 1,350 billion m³ of water from the GBM [Ganges, Brahmaputra, and Meghna] basin drains through the country.

From World Bank 2011

Figure 2

I recommend this World Bank report, very interesting, and you can see some idea of the costs of mitigating against floods. These problems are already present – floods are a regular occurrence, some mitigation has already taken place, and more mitigation continues.

I read the entire report and all I could find was that rising sea levels would exacerbate the problems already faced from storm surges: p.6:

Increase in ocean surface temperature and rising sea levels are likely to intensify cyclonic storm surges and further increase the depth and extent of storm surge induced coastal inundation.

However, the projections indicate that sea level rise is much less of a problem compared with possible increases in future storm surges and possible increases in future flooding. And compared with current storm surges and current flooding. We will look at floods and storm surges in future articles.

In the report it’s clear that floods and storms are already major problems. Sea level is harder to analyze. Trying to account for a sea level rise of 0.3m by 2050 when severe storm surges are already 5-10m is not going to make much of a difference. If we had accurate prediction of storm surges, to +/- 0.3m, then sea level rise of 0.3m should definitely be accounted for. But we don’t have anything like that kind of accuracy.

Well, they do some calculations of adaption against storm surges for projected changes up to 2050:

Under the baseline scenario, the adaptation costs total $2.46 billion. In a changing climate, the additional adaptation cost totals US$892 million.

In essence the question is “what is the storm surge for a once in a 10 year storm in 2050”? (I’m sure Bangladesh would really prefer to build protection against a once in 100 year storm). An extra $1bn for future problems, or a total of $3.5bn to cover existing and future problems, seems like money that would be very well spent, representing excellent value.

Nicholls and Cazenave (2010), in relation to the susceptible coastline of Asia and Africa, comment on adaption:

Many impact studies do not consider adaptation, and hence determine worst-case impacts. Yet, the history of the human relationship with the coast is one of an increasing capacity to adapt to adverse change. In addition, the world’s populated coasts became increasingly managed and engineered over the 20th century. The subsiding cities discussed above all remain protected to date, despite large relative SLR.

Analysis based on benefit-cost methods show that protection would be widespread as well-populated coastal areas have a high value and actual impacts would only be a small fraction of the potential impacts, even assuming high-SLR (>1 m/century) scenarios. This suggests that the common assumption of a widespread forced retreat from the shore in the face of SLR is not inevitable. In many densely populated coastal areas, communities advanced the coast seaward via land claim owing to the high value of land (e.g., Singapore).

Yet, protection often attracts new development in low lying areas, which may not be desirable, and coastal defense failures have occurred, such as New Orleans in 2005. Hence, we must choose between protection, accommodation, and planned retreat adaptation options. This choice is both technical and sociopolitical, addressing which measures are desirable, affordable, and sustainable in the long term. Adaptation remains a major uncertainty concerning the actual impacts of SLR.

In the World Bank 2011 report, in chapter 4, after their analysis on risks and costs of storm-induced inundations in 2050 resulting from projected higher cyclonic wind speeds and a projected increase in sea level of 0.27m, they comment, p.24:

As a cautionary note, it should be noted that this analysis did not address the out-migration from coastal zones that a rise in sea level and intensified cyclonic storm surges might induce.

In fact the cost data assumes population growth in the vulnerable regions.

Likewise, here is Hinkel et al (2014):

Coastal flood damages are expected to increase significantly during the 21st century as sea levels rise and socioeconomic development increases the number of people and value of assets in the coastal floodplain.

[Emphasis added].

This assumption bias creates an interpretation challenge. It would be useful to see notes to the effect: “If the population migrates away from this area due to the higher risk, instead the cost will be $X assuming a reduction of Y% in population in this region by 2050“. This extra item of data would create a useful contrast and I’m guessing that we would see impact assessments reduce by a factor of 5 or 10.

It is difficult to see realistic global sea level changes, even to the end of the century, having a big impact on Bangladesh compared with their current problems of annual flooding and frequent large storm surges. Of course, adding an extra 0.5m to the sea level doesn’t improve the situation, but it is an order of magnitude smaller than storm surges.

The adaption costs estimated by the World Bank to protect against storm surges (already required but at least a work in progress) seem moderate in value.

Lastly, I wasn’t able to find a detailed elevation map (with, say, 0.5m resolution), instead the ones I found graded the elevation with respect to sea level in fairly coarse steps. I’m sure the information exists but may be proprietary (in GIS data for example):

Figure 2 – Click to expand

I have to admit that I believed something like 25% of the Bangladesh population were around 1.0m or less above current sea level. This map says that the 0-3m area is quite small. If anyone does have a better resolution map I will post it up.

So the cost of sea level rise for 2100 in the US seems to be a close to zero cost problem.

Probably the provocative way I wrote the conclusion confused some people. I should have said that it was a very expensive problem. But that it wasn’t a problem that society should pay for, given that anyone moving to the coast since 2005 at the latest would have known that future sea level was considered to be a major problem. By 2100 the youngest people still living right on the sea front, who bought property there before 2005, would be at least 115 years old.

The idea is that “externalities” as economists call them should be paid by the creators of the problem, not the people that incur the problem. In this case, the “victims” are people who ignored the evidence and moved to the coast anyway. Are they still victims? That was my point.

Well, what about outside the US?

Some mega cities have huge problems. Here is Nicholls 2011:

Coastal areas constitute important habitats, and they contain a large and growing population, much of it located in economic centers such as London, New York, Tokyo, Shanghai, Mumbai, and Lagos. The range of coastal hazards includes climate-induced sea level rise, a long-term threat that demands broad response.

Global sea levels rose 17 cm through the twentieth century, and are likely to rise more rapidly through the twenty-first century when a rise of more than 1 m is possible.

In some locations, these changes may be exacerbated by

(1) increases in storminess due to climate change, although this scenario is less certain
(2) widespread human-induced subsidence due to ground fluid withdrawal from, and drainage of, susceptible soils, especially in deltas.

Subsidence?

Over the twentieth century, the parts of Tokyo and Osaka built on deltaic areas subsided up to 5 m and 3 m, respectively, a large part of Shanghai subsided up to 3 m, and Bangkok subsided up to 2 m.

This human-induced subsidence can be mitigated by stopping shallow, subsurface fluid withdrawals and managing water levels, but natural “background” rates of subsidence will continue, and RSLR will still exceed global trends in these areas. A combination of policies to mitigate subsidence has been instituted in the four delta cities mentioned above, combined with improved flood defenses and pumped drainage systems designed to avoid submergence and/ or frequent flooding.

In contrast, Jakarta and Metro Manila are subsiding significantly, with maximum subsidence of 4 m and 1 m to date, respectively (e.g., Rodolfo and Siringan, 2006; Ward et al., 2011), but little systematic policy response is in place in either city, and future flooding problems are anticipated.

Subsidence graphic:

From Nicholls 2011

Figure 1

To put these figures in context, sea level rise from 1900-2000 was about 0.2m and according to the latest IPCC report the forecast of sea level rise by 2100 might be around an additional 0.5m (for RCP 6.0, see earlier article). In the light of the idea that global society should pay for problems to people caused by global society, perhaps the problems of Shanghai, Bangkok and other sinking cities are not global problems?

Here is Wang et al from 2012:

Shanghai is low-lying, with an elevation of 3–4 m. A quarter of the area lies below 3 m. The city’s flood-control walls are currently more than 6 m high. However, given the trend of sea level rise and land subsidence, this is inadequate. Shanghai is frequently affected by extreme tropical storm surges. The risk of flooding from overtopping is considerable..

..From 1921 to 1965, the average cumulative subsidence of the city center was 1.76 m, with a maximum of 2.63 m. From 1966 to 1985, a monitoring network was established and subsidence was mitigated through artificial recharge. Land subsidence was stabilized at an average of 0.9 mm/year. As a result of rapid urban development and large-scale construction projects between 1986 and 1997, subsidence of the downtown area increased rapidly, at an average rate of 10.2 mm/year..

..In 2100, sea level rise and land subsidence will be far greater than before. Sea level rise is estimated to be 43 cm, while land subsidence is estimated to be 3–229 cm, and neotectonic subsidence is estimated to be 14 cm. Flooding will be severe in 2100 (Fig. 8).

[Note I changed the data in the last paragraph cited to round numbers in cm from their values quoted to 0.01cm – for example, 43cm instead of the paper’s values of 43.31 etc].

So for Shanghai at least global sea level rise is not really the problem.

Given that I don’t pay much attention to media outlets I probably missed the big Marches against Ground Water Depletion Slightly Accentuating Global Warming’s Sea Level Rise in Threatened Megacities.

As with the USA data the question of increased storm surges accentuating global sea level rise is still on the agenda (i.e., has not yet been discussed).

In Parts VI and VII we looked at past and projected sea level rise. It is clear that the sea level has risen over the last hundred years, and it’s clear that with more warming sea level will rise some more. The uncertainties (given a specific global temperature increase) are more around how much more ice will melt than how much the ocean will expand (warmer water expands). Future sea level rise will clearly affect some people in the future, but very differently in different countries and regions. This article considers the US.

A month or two ago, via a link from a blog, I found a paper which revised upwards a current calculation (or average of such calculations) of damage due to sea level rise in 2100 in the US. Unfortunately I can’t find the paper, but essentially the idea was people would continue moving to the ocean in ever increasing numbers, and combined with possible 1m+ sea level rise (see Part VI & VII) the cost in the US would be around $1TR (I can’t remember the details but my memory tells me this paper concluded costs were 3x previous calculations due to this ever increasing population move to coastal areas – in any case, the exact numbers aren’t important).

Two examples that I could find (on global movement of people rather than just in the US), Nicholls 2011:

..This threatened population is growing significantly (McGranahan et al., 2007), and it will almost certainly increase in the coming decades, especially if the strong tendency for coastward migration continues..

And Anthoff et al 2010

Fifthly, building on the fourth point, FUND assumes that the pattern of coastal development persists and attracts future development. However, major disasters such as the landfall of hurricanes could trigger coastal abandonment, and hence have a profound influence on society’s future choices concerning coastal protection as the pattern of coastal occupancy might change radically.

A cycle of decline in some coastal areas is not inconceivable, especially in future worlds where capital is highly mobile and collective action is weaker. As the issue of sea-level rise is so widely known, disinvestment from coastal areas may even be triggered without disasters..

I was struck by the “trillion dollar problem” paper and the general issues highlighted in other papers. The future cost of sea level rise in the US is not just bad, it’s extremely expensive because people will keep moving to the ocean.

Why are people moving to the coast?

So here is an obvious take on the subject that doesn’t need an IAM (integrated assessment model).. Perhaps lots of people missed the IPCC TAR (third assessment report) in 2001. Perhaps anthropogenic global warming fears had not reached a lot of the population. Maybe it didn’t get a lot of media coverage. But surely no could have missed Al Gore’s movie. I mean, I missed it from choice, but how could anyone in rich countries not know about the discussion?

So anyone since 2006 (arbitrary line in the sand) who bought a house that is susceptible to sea level rise is responsible for their own loss that they incur around 2100. That is, if the worst fears about sea level rise play out, combined with more extreme storms (subject of a future article) which create larger ocean storm surges, their house won’t be worth much in 2100.

Now, barring large increases in life expectancy, anyone who bought a house in 2005 will almost certainly be dead in 2100. There will be a few unlucky centenarians.

Think of it as an estate tax. People who have expensive ocean-front houses will pass on their now worthless house to their children or grandchildren. Some people love the idea of estate taxes – in that case you have a positive. Some people hate the idea of estate taxes – in that case strike it up as a negative. And, drawing a long bow here, I suspect a positive correlation between concern about climate change and belief in the positive nature of estate taxes, so possibly it’s a win-win for many people.

Now onto infrastructure.

From time to time I’ve had to look at depreciation and official asset life for different kinds of infrastructure and I can’t remember seeing one for 100 years. 50 years maybe for civil structures. I’m definitely not an expert. That said, even if the “official depreciation” gives something a life of 50 years, much is still being used 150 years later – buildings, railways, and so on.

So some infrastructure very close to the ocean might have to be abandoned. But it will have had 100 years of useful life and that is pretty good in public accounting terms.

Why is anyone building housing, roads, power stations, public buildings, railways and airports in the US in locations that will possibly be affected by sea level rise in 2100? Maybe no one is.

So the cost of sea level rise for 2100 in the US seems to be a close to zero cost problem.

These days, if a particular area is recognized as a flood plain people are discouraged from building on it and no public infrastructure gets built there. It’s just common sense.

Some parts of New Orleans were already below sea level when Hurricane Katrina hit. Following that disaster, lots of people moved out of New Orleans to a safer suburb. Lots of people stayed. Their problems will surely get worse with a warmer climate and a higher sea level (and also if storms gets stronger – subject of a future article). But they already had a problem. Infrastructure was at or below sea level and sufficient care was not taken of their coastal defences.

A major problem that happens overnight, or over a year, is difficult to deal with. A problem 100 years from now that affects a tiny percentage of the land area of a country, even with a large percentage (relatively speaking) of population living there today, is a minor problem.

Perhaps the costs of recreating current threatened infrastructure a small distance inland are very high, and the existing infrastructure would in fact have lasted more than 100 years. In that case, people who believe Keynesian economics might find the economic stimulus to be a positive. People who don’t think Keynesian economics does anything (no multiplier effect) except increase taxes, or divert productive resources into less productive resources will find it be a negative. Once again, drawing a long bow, I see a correlation between people more concerned about climate change also being more likely to find Keynesian economics a positive. Perhaps again, there is a win-win.

In summary, given the huge length of time to prepare for it, US sea level rise seems like a minor planning inconvenience combined with an estate tax.